Grading meningiomas by used imaging features on magnetic resonance imaging

Abstract

Background: Meningioma's are by far the most frequent primary tumors occurring inside the cranium. Prognostic wise, the problem with grade II and grade III neoplasms is the high recurrence rate following surgical resection. Surgical goal is to offer complete resection of the tumor to avoid future recurrence;however, such complete resection is limited by a number of factors such as the tumor location with the central nervous system, invasion of underlying vital brain tissue, involvement of cranial nerves and invasion of dural sinuses. Therefore, pre-operative imaging assessment of meningioma is necessary and the ability to grade these tumors on sole imaging background is an essential step in order to select the optimum surgical and or radiation based therapy. Aim of the study: In the current systemic review we collected information about radiological features detected by MRI techniques and analyzed these futures statically with respect to accuracy, sensitivity and specificity aiming at providing an MRI criteria predicting atypical grade II and III meningiomas prior to surgical intervention. Materials and methods: The current systematic review was based on the preferred reporting items for systematic reviews and meta-analysis guidelines. The primary objective of the current review was to evaluate the currently published data on the potential role of the technique such as Diffusion Weighted Imaging (DWI), and Apparent Diffusion Coefficient (ADC) for the assessment of meningioma grade. The principal research questions were: 1.What is the sensitivity of magnetic resonance imaging in Grading meningioma for brain?. 2. What is the specificity of magnetic resonance imaging in Grading meningioma for brain?. Results: We found that peritumoral edema, tumor necrosis, apparent diffusion coefficient, diffusion weighted trace, tumor enhancement, dural tail, tumor margin and tumor brain interface are all associated with significant prediction potential with respect to meningioma grade (p<0.05). On the other hand, capsular enhancement, T1-weighted imaging, T2-weighted imaging and tumor location are all insignificant predictors of high grade tumor (p>0.05). Highest sensitivity was seen in association with peritumoral edema (73.0 %). Highest specific level was seen in association with Apparent Diffusion Coefficient (ADC) (90.4 %). Tumor necrosis was associated with highest PPV (61.9 %). Highest negative predictive value was seen in association with shape of tumor margin (85.7 %) and highest level of accuracy was observed in association with tumor brain interface (78.2 %). Conclusion: A number of imaging characteristics in MRI can predict the grade of meningioma prior to surgical intervention including peritumoral edema, tumor necrosis, apparent diffusion coefficient, diffusion weighted trace, tumor enhancement, dural tail, tumor margin and tumor brain interface and the presence of any combination of these characteristics will make the decision even more precise. Copyright: